AI Is Nothing Without a Good Business Case

3 minute read

AI is a major buzzword within today’s market. But that doesn’t mean companies will automatically see ROI just by grabbing and implementing anything that vaguely resembles “AI.” You still need a good business case.

In the 1990s, the rise of the Web was a disruptive force which completely upended the way companies operate. Nonetheless, that didn’t mean that setting up a website instantly resulted in a sustainable business model (as many early dot-com companies would come to find out the hard way — remember Transformative technologies still need to be partnered with a robust business plan.

Fast forward to today and Artificial Intelligence (AI) is the modern-day disruptor du jour. And with good reason: AI is poised to reinvent business operations by intelligently executing processes at scale and allowing users to engage with digital systems through a human-like interface. Consider the quantifiable results Accenture Japan experienced when it hired Amelia to take on the role of on-demand HR concierge.

Just as adding a website didn’t guarantee success in the 1990s, beginning an AI journey without a proper business plan is not a harbinger of instant growth and achievement. When it comes to digital transformation – that is, incorporating modern digital solutions into an organization including AI – ROI is far from certain without good planning. A survey from management consulting firm Bain found that “just 5% of those companies involved in digital transformation efforts reported that they had achieved or exceeded the expectations they had set for themselves” and even worse “a full 75% of these companies settled for dilution of value and mediocre performance.”

When it comes to beginning an AI journey, the key to success is crafting a clear and actionable business plan that includes concrete goals, proper investment resources and organizational transition plans to support a sustainable effort.

Be deliberate with your data

As we’ve previously written, data is the lifeblood of AI. An AI system needs lots of data for training to behave in a way that will benefit your business. Once the system is up and running, it can then easily transform facts and figures into value with machine efficiency. While large amounts of data are a prerequisite for getting AI up and running, that doesn’t mean you should just slap an AI solution onto any existing pool of data and hope for the best.

In today’s connected world, everything we do leaves a digital trail, which when combined with AI can be used to create value-generating applications. However, this capability still needs to be focused. A researcher from Gartner observed that around 85% of “Big Data” projects fail and went on to note that it wasn’t a specific product’s fault as much as it was internal challenges, such as integrating into existing business processes, and management resistance among other concerns.

If your company has a lot of data (and most do), that alone shouldn’t be a reason to implement AI. Decision makers should take stock of their organization’s challenges and customer pain points, and identify which ones associated with large amounts of data and which could be improved by implementing an AI system.

Build a sustained AI strategy

AI automates business processes, allowing companies to reduce overhead in specific areas. Regardless of the initial savings of AI implementations, companies need to invest in a sustained use case development effort — and then maintain it over time.

In previous posts, we’ve provided guidance on building an internal and multi-disciplinary Cognitive Center of Excellence (CCoE) to sustain the value of cognitive AI platforms such as Amelia. Equally important is identifying the right AI partner to guide you on an AI journey. AI holds the promise of substantial ROI, but any proper AI project needs to be viewed as a sustained endeavor rather than a mere one-and-done investment.

In addition to creating clear goals and building an infrastructure to sustain your AI vision, it’s also important to manage internal company change by finding an internal AI Champion who can clearly identify, communicate and maintain AI’s value in the long-term. Similarly, it is important to manage expectations — in particular to stay ahead of naysayers, as well as unrealistic or overzealous ROI targets.

There were undoubtedly cultural barriers that stopped some companies from fully embracing the Web back in the '90s. However, when you couple a solid, well thought-out business plan with a sustained long-term effort, you improve your chances of transforming your business and looking more like an AI innovator, rather than the next

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